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Abstract

Background

Carbon dioxide (CO2) is a pervasive chemical stimulus that plays a critical role in insect life, eliciting behavioral and physiological responses across different species. High CO2 concentration is a major feature of termite nests, which may be used as a cue for locating their nests. Termites also survive under an elevated CO2 concentration. However, the mechanism by which elevated CO2 concentration influences gene expression in termites is poorly understood.

Methods

To gain a better understanding of the molecular basis involved in the adaptation to CO2 concentration, a transcriptome of Coptotermes formosanus Shiraki was constructed to assemble the reference genes, followed by comparative transcriptomic analyses across different CO2 concentration (0.04%, 0.4%, 4% and 40%) treatments.

Results

(1) Based on a high throughput sequencing platform, we obtained approximately 20 GB of clean data and revealed 189,421 unigenes, with a mean length and an N50 length of 629 bp and 974 bp, respectively. (2) The transcriptomic response of C. formosanus to elevated CO2 levels presented discontinuous changes. Comparative analysis of the transcriptomes revealed 2,936 genes regulated among 0.04%, 0.4%, 4% and 40% CO2 concentration treatments, 909 genes derived from termites and 2,027 from gut symbionts. Genes derived from termites appears selectively activated under 4% CO2 level. In 40% CO2 level, most of the down-regulated genes were derived from symbionts. (3) Through similarity searches to data from other species, a number of protein sequences putatively involved in chemosensory reception were identified and characterized in C. formosanus, including odorant receptors, gustatory receptors, ionotropic receptors, odorant binding proteins, and chemosensory proteins.

Discussion

We found that most genes associated with carbohydrate metabolism, energy metabolism, and genetic information processing were regulated under different CO2 concentrations. Results suggested that termites adapt to ∼4% CO2 level and their gut symbionts may be killed under high CO2 level. We anticipate that our findings provide insights into the transcriptome dynamics of CO2 responses in termites and form the basis to gain a better understanding of regulatory networks.

Introduction

Despite the low concentration of carbon dioxide (CO2) in air, it plays a critical role in insect life. Insects not only live in the normal atmosphere environment, but are also sometimes exposed to higher or lower CO2 concentrations. Naturally high CO2 concentration is likely to occur in holes under the bark of trees or stumps, in the soil when it is covered by ice and snow, or inside decomposing organic matter. Fluctuations of atmospheric CO2 could evoke behavioral and physiological responses in insects. On the one hand, CO2 acts as an attractive cue to elicit behavioral responses in many insects, such as seeking food and hosts, avoiding conspecifics, and locating nests (Guerenstein & Hildebrand, 2008). For example, mosquitoes depend on CO2 to locate human hosts whose volatile emissions contain CO2 (Gillies, 1980; Guerenstein & Hildebrand, 2008). Many moths measure the CO2 gradients, which indicate the floral quality, to find more and better nectar (Guerenstein et al., 2004; Thom et al., 2004). In Drosophila, high concentrations of CO2 elicit an avoidance response to other individuals (Suh et al., 2004). Social insects such as bees, wasps, ants and termites may detect CO2 concentration to locate their nests, in which CO2 concentration is much higher than the atmospheric concentration (Seeley, 1974). On the other hand, physiological effects of CO2 are diverse. In the nervous system, increasing CO2 concentration induce sub-lethal or lethal effects (Nicolas & Sillans, 1989). In the respiratory and circulatory system, changes in CO2 regulate the opening of the spiracles. In developmental processes, high CO2 may decrease metabolic rates, reduce weight, affect size, or prolong larval life and growth. In regards to reproduction, CO2 may delay or impede mating activity, accelerate oviposition, or stimulate vitellogenin synthesis (Nicolas & Sillans, 1989).

Termites contribute up to 2% of the natural efflux of CO2 from terrestrial sources (Sugimoto, Bignell & MacDonald, 2000) and 10% from the soil surface (De Gerenyu et al., 2015). High CO2 concentration is a major feature of termite nests. Inside the nests, termite activity takes place under an elevated CO2 concentration (0.3–5%) and occasionally up to 15%, but outside the nests, termites are exposed to the natural CO2 concentration in air (approximately 0.04%) (Ziesmann, 1996). It is suggested that CO2 concentration may provide information on location of termite nests. Bernklau et al. (2005) reported that Reticulitermes spp. were attracted to CO2 concentrations between 5 and 50 mmol/mol and CO2 could be used as an attractant in baiting systems to elicit termites to an insecticide. This finding has been commercialized and is used in Ensystex bait systems under the name Focus.

The chemosensory system is usually used by insects to sense odorants, the taste of food, or other chemical stimuli in the environment. Sensory structures for detecting changes in atmospheric CO2 have been identified and described in Lepidoptera, Diptera, Hymenoptera, and Isoptera (Stange & Stowe, 1999). The structures typically contain clusters of wall-pore type sensilla and are housed in pits or capsules. In different insects, they are located on either the palps (moths, mosquitoes, flies, and beetles) or the antennae (bees, ants, and termites) (Stange & Stowe, 1999). In termites, study of Schedorhinotermes lamanianus showed that sensory cells in the antennal sensilla may be sensitive to both CO2 and odorant (Ziesmann, 1996). The insect chemosensory proteins are various and mainly located in the sensory structures, such as odorant receptor (OR), gustatory receptor (GR), ionotropic receptor (IR), odorant binding protein (OBP), and chemosensory protein (CSP) families. Several studies have aimed to elucidate their underlying mechanisms and functions. The first study on the molecular bases of CO2 reception was in Drosophila. Two GR genes (GR21a and GR63a) were identified, and co-expression of them was necessary to confer a CO2 response (Jones et al., 2007; Kwon et al., 2007). Orthologues of GR21a and GR63a have been identified in butterfly, moth, beetle, mosquito, and termite species, but not in honeybees, pea aphids, ants, locusts and wasps (Xu & Anderson, 2015). These genomic differences may suggest different chemoreceptors and mechanisms for CO2 detection among different insects.

The objective of this study was to investigate the effects of elevated CO2 concentrations on the Formosan subterranean termite (Coptotermes formosanus Shiraki) in artificial, sealed chambers in the laboratory. Lower termite C. formosanus is among the most destructive species worldwide and characterized by the dependence on protozoan symbionts for cellulose digestion. In the present study, to enable comprehensive gene expression profiling, we generated as complete a reference transcriptome as possible for C. formosanus. Pooled RNA from different developmental stages and castes was used as starting material for Illumina sequencing. Next, we constructed four libraries of C. formosanus workers at different CO2 concentrations and compared gene expression profiles among them. We identified differentially expressed genes, analyzed sensitive processes that were involved in the response to elevated CO2, and screened genes associated with the chemosensory system. These assembled and annotated transcriptome sequences will facilitate gene discovery in C. formosanus and functional analysis of expressed genes and deepen our understanding of the molecular basis of responses to elevated CO2 concentrations in termites and other insects.

Materials & Methods

Insects and CO2 treatments

Colonies of C. formosanus termites, collected in Guangzhou International Biotech Island (23°04′01.71″N, 113°21′47.74″E), Guangdong, China, were kept in the laboratory in 5.0-L covered plastic boxes containing blocks of pine wood in 85 ± 5% humidity at 27 ±1 °C until they were used in experiments. No specific permissions were required for accessing these locations for sampling activities, and no endangered or protected species were involved in the study.

Sampling and RNA extraction

For collecting samples of RNA, untreated individuals (including the worker, soldier and reproductive castes) of C. formosanus from our laboratory were collected and frozen immediately in liquid nitrogen and stored in −80 °C freezers until use. The samples of termites were randomly chosen with development stages, including larva, worker, pre-soldier, and soldier. Each sample containing 50 whole body individuals from each caste and stage was subjected to RNA isolation. Samples of 50 live workers from each CO2 treatment were also collected and frozen immediately in liquid nitrogen and stored in −80 °C. Total RNA was extracted using the RNAsimple Total RNA Kit (TIANGEN, Beijing, China) according to the manufacturer’s instructions. RNA quantity and quality were assessed using the NanoDrop spectrophotometer (Nanodrop Technologies Inc., Rockland, DE, USA) and the Agilent 2100 Bioanalyzer (Santa Clara, CA, USA). The standards applied were OD260∕OD230 ≥ 1.8, 1.8 ≤ OD260∕OD280 ≤ 2.2, and RNA integrity number values >8.0. RNA samples were used for cDNA library construction and qRT-PCR.

cDNA library construction and sequencing

For reference transcriptome of C. formosanus, equal amounts of RNA from untreated samples (larva, worker, pre-soldier, soldier, and reproductive) and all CO2-treated samples were mixed, designated as Cfo. For transcriptomic comparison among CO2 treatments, RNA from 0.04%, 0.4%, 4%, and 40% CO2-treated workers were used, designated as T1, T2, T3, and T4, respectively. T1 was served as the control group. Finally, five library constructions (Cfo, T1, T2, T3, and T4) and the RNA sequencing were performed by the Biomarker Biotechnology Corporation (Beijing, China). Approximately, 5 µg of total RNA for each sample was used for the construction of libraries using TruSeq Stranded mRNA Sample Prep Kit (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s protocol. Sequencing was performed in a v3 flowcell on an Illumina HiSeq™ 2500 sequencer, using the TruSeq PE Cluster Kit v3 (Illumina PE-401-3001) and the TruSeq SBSHS Kit v3 200 cycles (Illumina FC-401-3001).

De novo transcriptome assembly and annotation

Raw reads were filtered by removing the adaptor sequences, empty reads and low quality sequences (reads with more than 50% of bases with Q-value ≤20). The clean reads were then assembled de novo using the Trinity platform (http://trinityrnaseq.github.io) with the parameters of ‘K-mer = 25, group pairs distance = 300′ (Grabherr et al., 2011). By performing pair-end joining and gap filling, contigs were assembled into transcripts, and the longest copy of redundant transcripts was regarded as a unigene (Grabherr et al., 2011; Haas et al., 2013).

The abundance of all genes was normalized and calculated by RSEM (Li & Dewey, 2011) and represented by the fragments per kilo base of transcript per million mapped reads (FPKM) value (Trapnell et al., 2010). We kept transcript isoform predictions whose FPKM > 0.03. DEGs were identified using EBSeq with conditions of FDR (False Discovery Rate) <0.01 and fold-change ≥2 (Leng et al., 2013). GO enrichment analysis of DEGs was implemented by using the Bioconductor package topGO (available at http://www.bioconductor.org/packages/release/bioc/html/topGO.html). Kolmogorov–Smirnov (KS) test was used to test the enrichment of GO terms with DEGs at a significance level of P ≤ 0.05 (Alexa & Rahnenführer, 2009). For the pathway enrichment analysis, we mapped all DEGs to terms in the KEGG database and looked for significantly enriched KEGG terms compared to the transcriptome database. We used KEGG Automatic Annotation Server (http://www.genome.jp/tools/kaas/) with the parameters of search program = ‘BLAST,’ GENEs data set = ‘for Eukaryotes, including auto-selected organisms and all insect organisms,’ and assignment method = ‘BBH (bi-directional best hit).’

Validation of RNA-Seq data

To confirm the differential expression of genes revealed by RNA-Seq, 10 DEGs were chosen for quantitative real-time PCR (qRT-PCR) validation. qRT-PCR was performed by using the TaKaRa SYBR® Premix Ex Taq Perfect Real Time qPCR Kit (TaKaRa, Japan) and the StrataGene Mx3000P QPCR System (Agilent Technologies, Santa Clara, CA, USA). For each gene, 100 ng of total RNA was used as a template in a mixture of specific primers (10 µM) (Table S1) and Master Mix to a final volume of 20 µL following manufacturer’s instruction. The qRT-PCR program was set to: 95 °C for 30 s of pre-incubation, 40 cycles of 95 °C for 5 s, 60 °C for 30 s, and 72 °C for 30 s of amplification. The specificity of the PCR products from each primer pair was confirmed by melting-curve analysis and agarose gel electrophoresis. Three biological replicates of each treatment were tested. All measurements were performed in triplicate. 18S ribosomal RNA was used as a reference gene to normalize gene expression according to previous study (Zhang et al., 2011). In addition, the expression of 18S rRNA in RNA-seq and preliminary qPCRs using the CO2-treated workers was stable (Fig. S1). The 2−ΔΔCt method was used to analyze the qRT-PCR data and assign relative expression differences (Livak & Schmittgen, 2001).

Results

Transcriptome sequencing and assembly

An overview of the sequencing and assembly is outlined in Table 1. After quality control, the number of clean bases in the reference transcriptome of C. formosanus, and four CO2 treatments T1, T2, T3, and T4 were 11.02, 2.30, 2.17, 2.24 and 2.21 GB, respectively, with an average GC content of 44.69% and a Q30 of 88.23% (Table 1). After assembly, 316,037 transcripts were completed and assembled into 189,421 unigenes. Many unigenes had a length between 200–1,000 bp. The mean length and N50 (50% of the transcriptome is in unigenes of this size or larger) length of unigenes were 629 bp and 974 bp, respectively. A larger N50 length and mean length are considered indicative of better assembly (Garg et al., 2011).

Functional annotation and classification

After annotation, the number of unigenes with different length annotated in different databases and their percentage were counted (Table S2). The NR database (61,407, 32.42%) had the largest match. The Swiss-Prot (35,633, 18.81%), PFAM (32,444, 17.13%), and KOG (30,531, 16.12%) shared similar quantities. Unigene length over 1,000 bp annotated more successfully than length less than 1,000 bp (Table S2).

Totally 16,552 unigenes were annotated into 55 sub-categories belonging to three main GO categories: biological process (BP), cellular component (CC), and molecular function (MF) (Fig. S2). There were 20, 19, and 16 sub-categories in BP, CC, and MF, respectively. The top sub-categories were metabolic process (10,208 unigenes), cell part (4,100 unigenes), and catalytic activity (9,975 unigenes) in BP, CC, and MF, respectively. By KOG classifications, 30,531 unigenes were classified functionally into 25 categories. The cluster of ‘signal transduction mechanisms’ was the largest group, which had 6,631 unigenes. Pathway analyses were also performed on all assembled unigenes to understand the biological functions of genes and how these genes interact. A total of 16,444 unigenes were functionally classified into five KEGG categories (Fig. S3): genetic information processing (5,403 unigenes, 788 enzymes), metabolism (2,169 unigenes, 487 enzymes), cellular processes (2,146 unigenes, 358 enzymes), environmental information processing (1,235 unigenes, 218 enzymes), and organismal systems (548 unigenes, 90 enzymes). Among 19 sub-categories, ‘translation,’ ‘transport and catabolism,’ and ‘folding, sorting and degradation’ were the top three sub-categories.

Because we made RNA-seq from whole termites containing guts, the transcriptome included host termite and symbiont genes. According to the NR species distribution result, there were 22,993 (37.44%) unigenes derived from insect species, which may be supposed to be termite genes, and 38,414 (62.55%) from protozoan symbionts. The distribution result was similar to the study by Zhang et al. (2012). Among termite genes, the majority of the sequences (50.31%) had strong homology with Zootermopsis nevadensis, followed by C. formosanus (8.22%), Tribolium castaneum (3.61%), Harpegnathos saltator (3.22%), Acyrthosiphon pisum (2.13%) and the remaining species were less than 2% (Fig. 1A). Among symbiont genes, the majority of the sequences (56.72%) had strong homology with genus Trichomonas, followed by genus Toxoplasma (3.86%) and Leishmania (3.37%) (Fig. 1B).

Transcriptome profiles of worker termites at different CO2 concentrations

Gene expression of all unigenes in T1, T2, T3, and T4 were estimated as FPKM. Genes with FPKMs ≤ 1 were considered not to be expressed or to be present at very low levels; genes with FPKMs over 60 were considered to be expressed at a very high level (Fang et al., 2015). Table 2 shows the distribution of expression levels of all genes in each CO2 treatments; the overall trend has been a decline as elevated CO2 concentrations (Table 2). The number of the genes with FPKM >1 shared by T1, T2, T3, and T4 were 24,385 (Fig. S4A) and the four samples had 876 common genes with high expression (FPKM > 60) (Fig. S4B). We analyzed the biological function of the highly expressed genes using the GO (Fig. S2) and KOG classifications. In the GO classification, the most abundant GO terms were ‘metabolic process’ and ‘catalytic activity.’ In the KOG classification, these genes were mainly classified into ‘translation, ribosomal structure and biogenesis,’ ‘posttranslational modification, protein turnover, chaperones,’ ‘cytoskeleton,’ and ‘energy production and conversion.’ Their functions covered metabolism, cellular processes and signaling, and information storage and processing. Thus, these genes may play an essential role in the life of termites. We found that three genes, c155263_c0, c190637_c0, and c188048_c3, were extremely highly expressed (FPKM > 9,000) in all four treatments. Gene c155263_c0 was annotated as a hypothetical protein with unknown function. Gene c190637_c0 was similar to ABC-type transporter Mla, which maintains outer membrane lipid asymmetry and participates in cell wall/membrane/envelope biogenesis. Gene c188048_c3 encoded endo-β-1,4-glucanase of C. formosanus, which is important to termite cellulose digestion system.

Differentially expressed genes (DEGs) and functional annotation

Hierarchical clustering of all DEGs was performed to observe the gene expression patterns based on the log2 FPKMs for the four samples (Fig. 2). The number of DEGs in each pairwise comparison is presented in Fig. 3. In total, all six comparison sets had 2,936 unique DEGs, 909 were termite DEGs and 2,027 were symbiont DEGs. The number of symbiont DEGs was more than twice greater than the number of termite DEGs, suggesting symbionts changed more remarkably than termite. Approximately 90% DEGs were in comparison sets of T1 vs. T4, T2 vs. T4, and T3 vs. T4, and a majority of them were down-regulated, especially in symbionts. However, in T1 vs. T3 and T2 vs. T3, the number of up-regulated termite DEGs was about twice and four times as many as the number of down-regulated termite DEGs, respectively. Meanwhile, the fold-change of up-regulated termite DEGs was larger than down-regulated termite DEGs in above two comparison sets (Table 3), which suggests genes are slightly up-regulated in T3 in termite but not symbionts.

According to GO classification (Fig. 4), the number of DEGs in some GO terms (e.g., ‘oxidation reduction,’ ‘alcohol metabolic process,’ ‘ion binding’ and ‘oxidoreductase activity’) was similar between termites and symbionts. But in most GO terms, the number of symbiont DEGs was more than the number of termite DEGs, such as ‘cell cycle process,’ ‘embryonic development,’ ‘growth,’ ‘macromolecule localization,’ ‘transferase activity,’ and ‘ligase activity.’ In KOG classification, the majority of termite DEGs are in the class ‘signal transduction mechanisms,’ ‘lipid transport and metabolism’ and ‘amino acid transport and metabolism,’ while the majority of symbiont DEGs are in the class ‘posttranslational modification, protein turnover, chaperones,’ ‘signal transduction mechanisms’, ‘translation, ribosomal structure and biogenesis’, and ‘cytoskeleton.’

Figure 4: Gene Ontology classification of termite and symbiont DEGs.

Compared to natural CO2 level (T1 vs. T2, T1 vs. T3, and T1 vs. T4), there were 54 common termite DEGs in response to elevated levels of CO2 (Fig. 5A, Table S3). Only two DEGs were up-regulated in all three sets. They were annotated as transferrin-like protein (c188927_c0) and prolixicin antimicrobial protein (c127508_c0). Thirty DEGs were down-regulated in all three sets, but only 15 had informative annotations (Table S3). Most of the commonly down-regulated DEGs were annotated as cuticle protein (10 DEGs), which contributes to the structural integrity of a cuticle and takes part in cell wall/membrane/envelope biogenesis. The rest of the commonly down-regulated DEGs included apolipoprotein D, which also participates in cell wall/membrane/envelope biogenesis (c102424_c0); collagen precursor, which is involved in extracellular structures (c181121_c0); glucokinase 1, which has transferase activity and participates in cellular metabolic process (c186958_c0); and actin cytoskeleton-regulatory complex protein (c127831_c0 and c169839_c0). Furthermore, 17 common DEGs were down-regulated in T2 and T4 but significantly up-regulated in T3. Among them, ten DEGs were annotated and mainly had three types of function: cuticle protein (c185045_c1, c126213_c0, c174474_c1, c190969_c1), fibroin heavy chain precursor (c128561_c0, c128751_c0, c192228_c0), and period circadian protein (c126015_c0, c174457_c0). For symbiont DEGs, there were 11 DEGs in common (Fig. 5A), 10 of them were down-regulated in T2, T3 or T4 compared to T1, such as c185407_c0 (annotated as cellulase) and c195974_c0 (annotated as ferredoxin-NADP oxidoreductase). Only one gene, c129705_c0 (annotated as threonine dehydratase family protein), was up-regulated in T4.

Figure 5: Effects of the elevated CO2 treatments on the Coptotermes formosanus transcriptome.

(A) Venn diagram showing the overlaps between the DEGs in elevated CO2 levels and normal air. (B) Venn diagram of the DEGs in T1, T2, and T3 compared to T4. The star (⋆) represent termite DEGs and the triangle (▴) represent symbiont DEGs.

Compared to high CO2 level (T1 vs. T4, T2 vs. T4, and T3 vs. T4), we found that 346 termite genes were commonly differentially expressed, with 74 up-regulated and 268 down-regulated in all three sets (Fig. 5B). Of the 74 up-regulated DEGs, 41 of them had informative annotations. For example, genes c184494_c2, c105191_c0, and c183958_c0 were highly expressed and associated with lipid transport and metabolism; c173654_c0 was highly expressed and involved in energy production and conversion. Among 268 down-regulated DEGs, 197 received informative annotations, 71 were hypothetical protein or uncharacterized protein. For example, gene c183902_c0, c174002_c0 and c168998_c0 were significantly down-regulated and participated in carbohydrate transport and metabolism. Moreover, we found that most common DEGs were only differential expressed in high CO2 level. A total of 64 up-regulated DEGs did not differentially expressed among T1, T2, and T3. For example, two DEGs (c81973_c0 and c174294_c0) were expressed only in T4. Three DEGs (c192331_c0, c180536_c0 and c127808_c0) were not expressed in T1 and T2 but were significantly expressed in T4. However, these five genes were annotated as hypothetical proteins in Z. nevadensis. Furthermore, there were 263 down-regulated DEGs that were not differentially expressed among T1, T2, and T3. For example, six genes (c192338_c0, c128228_c0, c129383_c0, c192671_c0, c192511_c0 and c184316_c0) showed high expression in T1, T2, and T3 (FPKM > 15) and low expression in T4 (FPKM < 1). Gene c192338_c0 is annotated as glyceraldehyde-3-phosphate dehydrogenase and plays a role in carbohydrate transport and metabolism. Gene c128228_c0, c129383_c0 and c192671_c0 are ribosomal proteins, which participate in translation, ribosomal structure and biogenesis. For symbiont DEGs, there were 1,028 DEGs in common, with 40 up-regulated and 988 down-regulated in all three sets. Among the down-regulated genes, 64 genes did not expressed in T4 (FPKM = 0), which take part in posttranslational modification, ribosomal structure, or cell wall biogenesis.

GO and KEGG enrichment analyses of the DEGs

The majority of significantly enriched GO terms were in T1 vs. T4, T2 vs. T4, and T3 vs. T4, specifically more than 130 GO terms enriched in biological process (Table S4). However, only two terms were common in biological process. The common enriched terms and the number of DEGs are listed in Table 4. Both termite and symbiont DEGs were enriched in ‘cytoplasm,’ ‘oxidoreductase activity,’ ‘metal ion binding,’ ‘iron ion binding,’ ‘oxidation–reduction process,’ and ‘transport.’ The termite DEGs were also enriched in ‘mitochondrial inner membrane,’ ‘heme binding,’ and ‘carbohydrate binding.’

Table 4:

Common enriched GO terms and number of DEGs derived from termites and symbionts.

In T1 vs. T2 and T2 vs. T3, the ‘oxidative phosphorylation’ pathway was significantly enriched and all DEGs were termite DEGs (Table 5). The ‘ribosome,’ ‘glycolysis/gluconeogenesis,’ and ‘starch and sucrose metabolism’ pathways were common enriched in T1 vs. T4, T2 vs. T4, and T3 vs. T4, however, the number of symbiont DEGs was larger than termite DEGs. ‘Aminoacyl-tRNA biosynthesis’ and ‘proteasome’ were classified in the KEGG ‘genetic information processing’ category, and were common enriched in T1 vs. T4 and T3 vs. T4. Both were changes of symbionts.

Expression profiles of chemosensory proteins

According to annotations and conserved protein domains, two ORs, five GRs, four IRs, 22 OBPs, and two CSPs were identified by the 7tm Odorant receptor (cl20237), 7tm chemosensory receptor (pfam08395), PBP2_iGluR_putative (cd13717), PBP/GOBP family (pfam01395), and insect pheromone-binding family OS-D (pfam03392) domain, respectively. Among these 35 genes, eight genes had a relatively high expression in at least one library (FPKM > 10), and most of them were up-regulated in T3 (Table S5). Six OBPs (c110031_c0, c128738_c1, c129041_c0, c192285_c0, c192783_c0, and c193269_c0) were significantly up-regulated in T3 compared to T1. One OBP, c128814_c0, was significantly increased in T3 compared to T2. One CSP, c125410_c0, was significantly increased in T3 compared to the other three libraries.

Validation of RNA-seq data by qRT-PCR

To validate the transcriptome result, we selected 10 DEGs for qRT-PCR confirmation (c125410_c0, c129041_c0, c166756_c0, c167200_c0, c168998_c0, c169342_c0, c173654_c0, c179746_c0, c181311_c0, and c184494_c2, five genes were described in the text). The primers used for qRT-PCR were shown in Table S1. The amplification efficiency of each primer set was validated; standard curves (10 × serial dilutions) yielded regression lines with R2 values > 0.97 and an amplification efficiency ranging from 0.9–1.1 (ideal value of 0.8–1.2). Each primer set produced a single amplicon as judged by the single peak in the dissociation curve. The qRT-PCR expression results (Fig. S5) were similar to the results obtained from the Illumina sequencing data. Three DEGs were highly expressed in T2 in the transcriptome results but minimally expressed in the qRT-PCR results. Although the expression levels were not completely consistent (possibly due to different methods of library construction, reference genes, normalization, or biological differences), the results fundamentally supported the reliability of the RNA-seq results.

Discussion

Overview of transcriptome data

C. formosanus, a worldwide important pest, has been studied extensively in omics, including genome, transcriptome, metabolome, DNA methylome, and 16S rRNA sequencing (Scharf, 2015). While most studies have focused on symbionts, a few have combined host and symbiont, considering the whole termite (Scharf, 2015). Those studies are mainly based on conventional Sanger sequencing; rarely has Illumina high-throughput sequencing study been reported to date. Compare to the study by Zhang et al. (2012) using Sanger sequencing, the present study newly assembled transcriptome contains massive amounts of data (11.02 GB) using Illumina sequencing, and covers different developmental stages and castes (larva, worker, pre-soldier, soldier, reproductive). The genetic information will facilitate future developmental and caste differential studies of C. formosanus, and contribute to future work in termite comparative genomics.

Transcriptomic response to elevated CO2 treatments

In this study, we exposed workers of C. formosanus to 0.04%, 0.4%, 4%, and 40% CO2 concentrations and constructed four transcriptomes to examine the gene expression profiles. Hierarchical clustering of all DEGs showed that the expression patterns of T1, T2, and T3 were very close, particularly T1 and T2; some DEGs were increased in T3; and more than one-third of DEGs showed reduced expression in T4 (Fig. 2). Since termites were collected and placed in a sealed container for 72 hr, the final CO2 level was higher than the initial concentration, which was 0.85% ± 0.07%, 1.11% ± 0.01%, 4.67% ± 0.01%, and 40.61% ± 0.04%, respectively. The order of the final CO2 concentration levels was still T1 < T2 < T3 < T4. However, the final T1 concentration was close to T2, which may result in the similar expression pattern of T1 and T2 (Fig. 2). The majority of the C. formosanus lifetime is spent living inside wood. The CO2 concentration in the nest, which was similar to the T3 treatment, is higher than it outside the nest. When termites go outside the nest, it is similar to the T1 or T2 treatment. Termites have adapted to a life in the nest or in enclosed galleries and are prone to perish quickly when exposed to the open atmosphere (Stange & Stowe, 1999). To some extent, this may be influenced by CO2 concentration, which may carry information relevant to termites, such as information on the location of their nest (Stange & Stowe, 1999). Thus, termites may increase gene expression and fit better in T3 treatment. The 40% of CO2 was abnormally high and some termites were dead after 72 h. Although we collected live termites for experiment, we cannot rule out the possibility that termites were damaged by CO2 exposure, suggesting that some changes in gene expression may be not directly associated with the CO2 effects. We also noted that symbionts, intestinal protists and bacteria, accounted for the majority of changes (69% DEGs derived from symbionts) and their expression mainly decreased in T4. Because high concentrations of CO2 might affect pH in the termite guts, and cause changes in intestinal flora. It is likely that the protists were killed by the abnormally high CO2 level, and as a result, gene expression levels of them were depressed. The death of protozoans may be CO2 direct effect, or combined effects of CO2 and other general stresses. However, the comparisons of transcript levels employed in our study are based on the assumption that total RNA content per cell remains constant. Lin et al. (2012) recently found transcriptional amplification in tumor cells with elevated c-Myc level, and Lovén et al. (2012) further indicated that many up-regulated DEGs were missed and down-regulated ones were falsely produced when processed by global normalizations. The extent to which this will force reconsideration of present expression studies is as yet unclear, especially the down-regulated DEGs. This problem will still be studied in the future.

To help understand the CO2 effects on termite biological processes and gene functions, termite DEGs were analyzed using the public databases. The over-represented GO terms were evaluated to infer which molecular functions, cellular components and biological processes were most affected by the experimental conditions (Table 4). For molecular function, elevated CO2 levels influenced oxidoreductase activity, metal ion binding, iron ion binding, heme binding, and carbohydrate binding. From studies in Drosophila and other insects, the receptors used to recognize olfactory stimuli appear to be ion channels, which may be associated with the enrichment of ion binding terms (Spehr & Munger, 2009). For the biological process, oxidation–reduction process and transport were affected, which may be linked to anaerobic respiration (Nielsen & Christian, 2007). Studies showed that gene expression may be suppressed to reduce oxygen, aerobic and metabolic activities, including oxidative phosphorylation, oxidation–reduction process, and carbohydrate metabolism in extremely high CO2 concentrations (Nielsen & Christian, 2007). From the KEGG enrichment results, we found that high CO2 levels significantly influenced ribosome, glycolysis/gluconeogenesis, and starch and sucrose metabolism pathways (Table 5). Briefly, there were three aspects effected by elevated CO2: (1) carbohydrate metabolism, such as the binding process, and substrates such as glucose, starch and sucrose; (2) energy metabolism, such as genes with oxidoreductase activity that take part in oxidation–reduction process and the oxidative phosphorylation pathway; and (3) the directed movement of substances (such as metal ion, iron ion, heme, and carbohydrate) by means of some agent such as a transporter or pore.

Genes associated with chemosensory system

In insect chemosensory systems, three chemosensory receptor multi-gene families (ORs, GRs, and IRs) are involved in detection, while OBPs and CSPs play a role in recognition (Brand et al., 2015). OR and GR proteins are highly diverse, with many sharing only 20% and 8% amino acid similarity, respectively (Hallem, Dahanukar & Carlson, 2006). The extraordinary divergence in sequences makes it difficult to detect and discriminate OR and GR genes by traditional sequencing methods. Insect OR and GR genes were first discovered in the genome sequence of Drosophila melanogaster, suggesting that these genes could largely be discovered from genome sequences. Thus, the transcriptome of C. formosanus may provide information on the candidate chemosensory genes. Totally, two ORs, five GRs, and four IRs were identified. The number of OR genes was obviously smaller than that of other insects, such as D. melanogaster, Anopheles gambiae, and Apis mellifera which have 60, 79, and 170 OR genes, respectively (Robertson & Wanner, 2006). One OR, c197137_c0, was homologous to Or83b of Holotrichia oblita, Plutella xylostella, Helicoverpa assulta, etc. Or83b is highly conserved among all insect species analyzed so far (Nakagawa et al., 2012). The number of GR genes was close to Ap. mellifera which has 10 GR genes, while D. melanogaster and An. gambiae have 60 and 79 GR genes, respectively (Robertson & Wanner, 2006). However, five GRs were not homologous to D. melanogaster GR21a or GR63a, and their expression was not significant under CO2 stress. Perhaps, GRs in C. formosanus do not act as CO2 receptors. However, we note that it is unlikely that the detected candidate genes represent the complete repertoire of the C. formosanus chemosensory gene families because detection is not possible if expression levels of target genes are too low or if they are specific to unexamined sexes, castes, life stages or tissues (Brand et al., 2015). The detected genes are likely important and typically among the highest expressed chemosensory genes in C. formosanus and thus are very likely to be detected in transcriptome analyses. However, more chemosensory genes and their functions should be examined in further experiments.

The two non-receptor multi-gene families, OBPs and CSPs, encode soluble proteins and have been identified in the lymph of chemosensilla and non-sensory organs in insects (Pelosi, Calvello & Ban, 2005). They contribute to the transport of odorant molecules through sensillar lymph, and increase the sensitivity and possibly the selectivity of the insect olfactory system (Leal, 2013). OBPs are reported to be different across species and within the same species, sharing even less than 20% amino acid identity in some cases (Zhou, Field & He, 2010). The number of OBP genes in different insects ranges from 15 (Acyrthosiphon pisum) to 66 (An. gambiae and Aedes aegypti) (Fan et al., 2011). In this study, we identified 22 OBP genes. According to their putative protein sequences, these OBPs could be divided into two groups: 11 were classical OBPs with six cysteine residues (Fig. 6A), and 11 were Minus-C OBPs with four or five cysteine residues (Fan et al., 2011; Pelosi et al., 2006). Among them, seven OBP genes were differentially up-regulated in T3, including five classical OBPs and two Minus-C OBPs. OBPs may perform roles either related or not related to chemoreception, as they are widely distributed throughout the insect’s body, including different sensory parts (e.g., antennae and mouth), tarsi and wings (Pelosi et al., 2006). However, the expression of receptor genes was inconsistent with OBP genes, which makes it difficult to explain. Both the response of OBP genes to elevated CO2 levels and the downstream response elements require more experiments. CSPs are smaller than OBPs and present a motif of four conserved cysteines (Angeli et al., 1999). The number of CSPs reported in each species is quite variable, such as Cactoblastis cactorum, Polistes dominulus, and Vespa crabro, with only one CSP, D. melanogaster with four CSPs, An. gambiae with seven CSPs, and Locusta migratoria with at least 20 CSPs (Pelosi et al., 2006). Here, we identified two CSP genes based on their sequences (Fig. 6B), and c125410_c0 was differentially up-regulated in T3. Some CSPs, such as CLP-1 of Cactoblastis cactorum and OS-D of Drosophila, have been reported to be involved in the perception of carbon dioxide (Maleszka & Stange, 1997; McKenna et al., 1994). Thus, the up-regulation of c125410_c0 may be in response to increased carbon dioxide.

Figure 6: Alignment of the partial amino acid sequence of Coptotermes formosanus OBPs and CSPs with that from other insects.

Odorant stimulation of olfactory receptor neurons results in a calcium influx modulating signal transduction pathways (Ronnett & Moon, 2002). Therefore, elevated CO2 levels may affect elements in signal transduction pathways. According to KEGG annotation, we found that four genes annotated as calmodulin in the olfactory transduction pathway (ko04740) were significantly down-regulated in T4 compared to T1, T2 and T3. Among them, calmodulin c181311_c0 had the highest FPKM value in all libraries, indicating that it is a major, common gene in the pathway. It encodes for a protein of 170 amino acids, characterized with two EF-hand or calcium binding motifs. There is evidence in the literature that the inhibition of calmodulin gene expression eliminates the CO2 gating sensitivity of connexin channels (Peracchia et al., 2003). Our results show that high CO2 concentration significantly suppresses calmodulin gene expression, while medium and low CO2 concentration have slight effect on the gene. However, the link between CO2 and calmodulin as well as the underlying mechanism need more experiments to illustrate.

Conclusion

Overall, we have identified 2,936 genes with dynamic regulation under elevated CO2 conditions belonging to diverse pathways, mainly metabolic processes and signal transduction. The candidate chemosensory proteins were also identified in C. formosanus, and some of them likely play a role in CO2 sensing. This preliminary study provide a number of candidate genes that may be used as starting point to dissect the gene regulatory network involved in termite responses to CO2.

Histogram of KEGG classifications of unigenes

Gene expression analyses by the Venn diagram

(A) The Venn diagram of the counts of genes with FPKM value more than 1 for four tests T1, T2, T3, and T4 from 84,531 genes. (B) The Venn diagram of the counts of genes with FPKM value more than 60 for four tests T1, T2, T3, and T4 from 1,315 genes.

Candidate genes related to chemosensory system

Acknowledgements

The authors thank the Biomarker Biotechnology Corporation (Beijing, China) for assisting with transcriptome sequencing. We are grateful to all people who in any way contributed to the development of this work.

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Wenjing Wu conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Zhiqiang Li conceived and designed the experiments, analyzed the data, wrote the paper, reviewed drafts of the paper.

Funding

This research was supported by the National Natural Science Foundation of China (NSFC) (31172163), Funds for Environment Construction & Capacity Building of GDAS’ Research Platform (2016GDASPT-0107), and Science and Technology Planning Project of Guangzhou city, China (201510010036). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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